Mobile network optimization

ABSTRACT

In one embodiment, a method implemented on a computing device includes: classifying a current coverage and capacity (CCO) status according to a multiplicity of performance factors for a multiplicity of mobile network cells, clustering the mobile network cells into cell clusters based on at least the classifying and proximity of the mobile network cells to each other, based at least on the performance factors, identifying at least one problem cluster from among the cell clusters, identifying at least one underperforming master key performance indicator (MKPI) for the at least one problem cluster, and instructing the mobile network cells in the at least one problem cluster to perform at least one remedial action to address at least one of the performance factors to improve performance according to the MKPI.

RELATED APPLICATION INFORMATION

The present application claims the benefit of priority from USProvisional Patent Application, Ser. No. 62/170,711, filed on Jun. 4,2015.

FIELD OF THE INVENTION

The present invention generally relates to optimizing mobile networkperformance and service.

BACKGROUND OF THE INVENTION

Self-optimizing networks (SONs) in the mobile space generally providereactive relief on a per cell basis to remedy downlink issues. When agiven cell exceeds a threshold failure rate for given period (e.g., toomany dropped connections in an hour), the SON reconfigures one or moresettings for the cell (e.g., transmission power is increased).

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the disclosure will be understood and appreciatedmore fully from the following detailed description, taken in conjunctionwith the drawings in which:

FIG. 1 is a partly pictorial, partly block diagram illustration of acoverage and capacity (CCO) server and associated mobile network cells,configured and operative in accordance with embodiments describedherein;

FIG. 2 is a flowchart of a process to be performed by the CCO server ofFIG. 1;

FIG. 3 is an exemplary CCO status classification tree to be used by oneof the steps of the process of FIG. 2;

FIG. 4 is an exemplary table of key performance indicator remedies to beused by one of the steps of the process of FIG. 2;

FIG. 5 A-C are alternative exemplary action timelines associated withsteps of the process of FIG. 2; and

FIGS. 6 A-C and 7 are graphs of observed performance of mobile networkcells demarked to illustrate details of embodiments described herein.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

A method implemented on a computing device includes: classifying acurrent coverage and capacity (CCO) status according to a multiplicityof performance factors for a multiplicity of mobile network cells,clustering the mobile network cells into cell clusters based on at leastthe classifying and proximity of the mobile network cells to each other,based at least on the performance factors, identifying at least oneproblem cluster from among the cell clusters, identifying at least oneunderperforming master key performance indicator (MKPI) for the at leastone problem cluster, and instructing the mobile network cells in the atleast one problem cluster to perform at least one remedial action toaddress at least one of the performance factors to improve performanceaccording to the MKPI.

Detailed Description of Example Embodiments

It will be appreciated by one of ordinary skill in the art that theadjustment of an individual cell's settings does not occur in a vacuum.Reconfiguring the settings for one particular cell may affect theperformance of other cells, particularly those positioned in relativeproximity to the reconfigured cell. The remedy for one cell maytherefore prove to be problematic for one or more neighboring cells,thereby generating an oscillating effect: the treatment of one cell mayincidentally serve to degrade the performance of a neighboring cellsufficiently to trigger the reconfiguration of its settings, which thenimpacts the performance of the first cell such that its settings arepresumably reconfigured a second time, and so on.

In accordance with embodiments described herein, mobile network coverageand capacity may be improved by clustering cells in accordance withcommon radio issues and addressing key performance issues on a percluster basis. It will be appreciated that by analyzing cell clusters,as opposed to individual cells, the identification of systemic issuesmay be facilitated, thereby enabling more timely and efficient treatmentof these issues, as well reducing exposure to the oscillating effectdescribed hereinabove.

Reference is now made to FIG. 1, which illustrates a coverage/capacityoptimization (CCO) server 100 and associated mobile network cells 10,constructed and operative in accordance with embodiments describedherein to optimize mobile network coverage and capacity. CCO server 100is arrayed in communication with a multiplicity of mobile network cells10 and comprises processor 110, I/O module 120, classifier 130,clusterer 140, CCO manager 150 and action database 155.

Processor 110 may be operative to execute instructions stored in amemory (not shown) in order to perform the herein described methods tooptimize mobile network coverage and capacity. I/O module 120 may be anyhardware or software component operative to use protocols such as areknown in the art to communicate with mobile network cells 10. I/O module120 may be implemented as a transceiver configured to transmit andreceive wirelessly and/or via a wired connection. Classifier 130,clusterer 140 and CCO manager 150 may be implemented in either hardwareor software and may be operative to be executed by processor 110 toperform at least the methods described herein for optimizing mobilenetwork coverage and capacity.

Reference is now made to FIG. 2 which illustrates a CCO process 200 tobe performed by CCO manager 150. CCO manager 150 invokes classifier 130to classify (step 210) a current CCO status for each mobile network cell10 in the coverage area of CCO server 100. It will be appreciated by oneof ordinary skill in the art that depending on design choices, a givenmobile network may be configured with more than one CCO server 100,where each mobile network cell 10 is assigned to one CCO server 100.

Reference is now also made to FIG. 3 which illustrates an exemplary CCOstatus classification tree 300 that is used by classifier 130 toclassify a current performance state for each of the mobile networkcells 10 in the target population. Each of the mobile network cells 10is classified in accordance with data received via I/O module 120 on CCOserver 100. The data may be provided by system components on mobilenetwork cells 10 and/or provided via “crowdsourcing” of the mobilecommunications devices using mobile network cells 10.

As depicted in FIG. 3, CCO status classification tree 300 addressespossible combinations of coverage and/or capacity issues ascharacterized by uplink/downlink interference to classify the currentstatus of each mobile network cell in terms of sixteen possible usecases. It will be appreciated that in some embodiments classificationmay employ a different combination of issues to classify a currentstatus. CCO status classification tree 300 illustrates how a currentstatus for each of four performance factors may be assessed byclassifier 130 to determine a relevant use case for a given mobilenetwork cell 10.

The downlink coverage for a mobile network cell 10 is assessed (step310) in terms of a definable threshold, such as, for example, whether anobserved received signal code power (RSCP) is less than or equal to −110dBm. The mobile network cell 10 is then assessed in terms of a definablethreshold for uplink interference (step 320A or 320B), for example,whether an observed received signal strength indication (RSSI) isgreater than or equal to −97 dBm. It will be appreciated by one ofordinary skill in the art that the assessment performed by classifier130 at steps 320A and 320B may be generally the same according to thesame definable threshold; the only material difference between steps320A and 320B may be a function of the previous assessment at step 310;i.e., if step 310 indicates that downlink coverage is unsatisfactory,then step 320A may be performed; otherwise, step 320B may be performed.

In similar fashion, each mobile network cell 10 may then be assessed interms of a definable threshold for downlink load (steps 330A-D), forexample, whether download utilization exceeds 80%. Each mobile networkcell 10 may then be assessed in terms of a definable threshold fordownload interference (steps 340A-H), for example, whether RSCP exceeds100 dBm while energy per chip/noise spectral density (Ec/No) is lessthan 13. Handovers to other radio access technologies (RATs) may also beindicative of download interference. It will be appreciated that thereare sixteen possible combinations of results from the performance ofsteps 310, 320, 330 and 340. Each of these combinations may define thecharacteristics of a use case representing a current CCO status for agiven mobile network cell 10.

For example, as depicted in CCO status classification tree 300, if aseries of negative results is received in steps 310, 320A, 330B and340D, the current CCO status may be classified as use case “H: noissue”; i.e., the indicated mobile network cell 10 may be assumed to beworking properly. Conversely, if a series of positive results isreceived in steps 310, 320B, 320C and 340E, the current CCO status maybe classified as use case “I: DL coverage & UL & DL Interference and DLload”; i.e., the indicated mobile network cell 10 may be assumed to besuffering from issues with all four of the assessed factors: downlinkcoverage, uplink interference, downlink load and downlink interference.It will be appreciated that the other use cases may indicate lessextreme statuses.

Returning to FIG. 2, CCO manager 150 invokes clusterer 140 to cluster(step 220) mobile network cells 10 in clusters based at least onphysical neighboring proximity (i.e., that the combined coverage areasof mobile network cells 10 in a given cluster form a continuousgeographic coverage area) and their use cases per step 210. Inaccordance with embodiments described herein, clusterer 140 may beconfigured to include a surrounding “envelope” of mobile network cells10 around a core of mobile network cells 10 with a common use case. Insuch manner, it may be possible to detect and fix issues caused inneighboring mobile network cells 10 that may be caused by remedialactions necessitated by a current status for mobile network cells 10 inthe core of the cluster. It will be appreciated by a person of ordinaryskill in the art, that in such manner, mobile network cells 10 may beincluded in more than one cluster; i.e., in a “primary cluster”, as wellas in the envelope of one or more “secondary clusters”.

It will be appreciated by one of ordinary skill in the art that not allof the mobile network cells 10 in a given cluster may necessarily havethe same exact use case. For example, as depicted in FIG. 3, use case“M” may differ from use case “N” in that use case “M” includes downlinkinterference whereas use case “N” does not. However, mobile networkcells 10 with use case “M” may still potentially be clustered withmobile network cells 10 with use case “N” based on the commonality ofthe other relevant factors, namely downlink coverage, anduplink/downlink interference.

CCO manager 150 may determine (step 230) a master key performanceindicator (MKPI) for the cluster. The MKPI may be determined as afunction of the use cases of the individual mobile network cells 10 inthe cluster. CCO manager 150 may then score (step 240) the determinedMKPI in terms of the potential gain in coverage and/or capacity that maybe realized if the situation was reversed. If the score is greater thana definable threshold (step 250) the MKPI may be used for furtherprocessing of the cluster. Otherwise, if other MKPI exist for thecluster (step 255), steps 230-250 may be repeated for an alternativeMKPI. If there are no more relevant MKPI to be scored, the cluster maybe discarded and the next cluster processed (step 259); i.e., CCOmanager 150 may determine that in terms of cost/benefit it may not beworthwhile to attempt to optimize the cluster. The MKPI may be selectedfrom among any suitable key performance indicator (KPI) known in theart, such as, for example, dropped call rates, hand over success/failurerates, call setup success/failure rates, etc. that are commonly used toassess retainability, accessibility, mobility, quality of experience,etc. It will be appreciated by one of ordinary skill in the art thatclusters with use case H, i.e., “no issue” may flow through to step 259without further processing.

CCO manager 150 may determine (step 260) remedial actions to beperformed based at least in part on the MKPI of each cluster. It will beappreciated by one of ordinary skill in the art that there may be morethan one potential cause for a given issue in a use case. Reference isnow made briefly to FIG. 4 which is a table of an exemplary series ofavailable remedial actions and options mapped to use case factors. Asdepicted in FIG. 4, each of the four assessed factors may be associatedwith multiple possible remedial actions. It will be appreciated by oneof ordinary skill in the art that the table is based on current vendorreleases. The feasibility of each particular option may be indicated foreach of three leading radio access network (RAN) vendors: Huawei (H),Nokia (N) and Ericsson (E) at the time of this application. Where thefeasibility is indicated as “Cross Domain”, core network implementationsmay also be available.

Returning to FIG. 2, CCO manager 150 may perform (step 270) one or moreremedial actions for the mobile network cells 10 of the cluster. Forexample, as per the exemplary series of FIG. 4, for use cases includinguplink interference, CCO manager 150 may employ one or more options toadjust uplink noise for mobile network cells 10 in the cluster.Similarly, CCO manager 150 may adjust the attitude of the antenna formobile network cells 10 in the cluster. The options selected for actionby CCO manager 150 may be determined at least in part with regard to thelevel of impact desired, i.e., impact on individual mobile network cells10 and/or on a cluster. In the absence of a restriction thatspecifically requires cell level action, the actions performed in step270 may generally be associated with cluster level impact. However, aswill be described hereinbelow with respect to step 295, correctiveactions may be performed as cell level actions unless cross domainactions are performed which by definition impact on the cluster level.It will be appreciated by persons of ordinary skill in the art that CCOmanager 150 may perform step 270 by sending instructions via I/O module120 to mobile network cells 10 in the cluster.

CCO manager 150 may monitor the performance of the cluster's cells todetermine the efficacy of the remedial action(s). If CCO manager 150detects a negative reaction, i.e., the overall effect of the remedialaction(s) is unsatisfactory as determined by a definable threshold (step280), CCO manager 150 may back out (step 285) the remedial action(s) toundo the remedial action(s). CCO manager 150 may also check for apartially acceptable reaction, where the remedial action(s) was/were atleast in part successful in improving performance, but at least onemobile network cell 10 in the cluster may have been adversely affected.For example, in a cluster with downlink (DL) coverage issues, CCOmanager 150 may select to increase antennae tilts. As a result, theinterference to envelope cells may increase and drop level consequentlyincrease for those cells 10 in the envelope.

If so (step 290), CCO manager 150 may perform (step 295) a correctiveaction to offset the adverse effect on the affected cells withoutbacking out the original remedial action(s) on the entire cluster. Asper the previous example, CCO manager 150 may increase the power curve(power per radio access bearer—RAB) for the envelope cells to offset theincreased interference, thereby reducing their drop levels. If there aremore clusters to process (step 299), processing control may return tostep 230 for the next cluster. Otherwise process 200 may end. It will beappreciated by one of ordinary skill in the art that process 200 may beperformed by CCO manager 150 on a periodic basis and/or on demand.

Reference is now made to FIG. 5A which illustrates an exemplary actiontimeline for monitoring the effects of actions performed in step 270. Itwill be appreciated by one of ordinary skill in the art that given thatthere may be multiple potential remedial actions to perform to remedy agiven issue, there may be different methods for prioritizing and/orordering the actions to be performed and/or back out by CCO manager 150.

In accordance with the embodiment of FIG. 5A, the performance of a givenmobile network cell 10 and/or cluster may be monitored during referencewindow 510. The monitored performance may serve as a baseline accordingto which the effects of the remedial actions of step 270 (FIG. 2) may beassessed. For example, in FIG. 5A the monitored performance may beindicated by the solid line, and the baseline performance level may beindicated by the dotted line.

In accordance with an exemplary embodiment, five remedial actions may beidentified for the cluster, each of which is performed at generally thesame point in time, action point 520. CCO manager 150 may then monitorthe combined effect of the five remedial actions on the performanceduring feedback window 530. If, as depicted in FIG. 5A, monitoredperformance during feedback window 530 drops below the baselineperformance level, CCO manager 150 may back out all of the remedialactions at back out point 540. CCO manager 150 may monitor the effect ofthe back out during back out window 550.

Reference is now made to FIG. 5B which illustrates an alternativeexemplary action timeline for monitoring the effects of actionsperformed in step 270. As described with reference to the embodiment ofFIG. 5A, a baseline performance level may be determined based onmonitoring during reference widow 510. However, instead of performingall of the remedial actions at generally the same, CCO manager 150 maybe alternatively configured to perform each remedial action separately.

For example, remedial action #1 may be performed at action point 520.CCO manager 150 may then monitor performance during feedback window 521to assess the effect of remedial action #1. Similarly, remedial actions#2-5 may be performed separately at action points 522, 524, 526 and 528,and their effects monitored during feedback windows 523, 525, 527 and529 respectively. If, as depicted in FIG. 5B, monitored performanceduring feedback window 530 drops after a specific remedial action suchas during feedback window 529 (i.e., following performance of remedialaction #5), CCO manager 150 may back out the specific remedial actionwhich was performed immediately prior to the monitored drop, i.e.,remedial action #5, during back out point 540. CCO manager 150 maymonitor the effect of the back out during back out window 550.

Reference is now made to FIG. 5C which illustrates yet anotheralternative exemplary action timeline for monitoring the effects ofactions performed in step 270. As described with reference to theprevious embodiments, a baseline performance level may be determinedbased on monitoring during reference widow 510. However, instead ofperforming all of the remedial actions either individually or at thesame time, CCO manager 150 may be alternatively configured to performtwo or more remedial actions at generally the same time, withoutnecessarily doing all of the remedial actions at the same time.

For example, remedial actions #1 and #3 may be performed at generallythe same time at action point 520. CCO manager 150 may then monitorperformance during feedback window 521 to assess the effect of remedialactions #1 and #3. Similarly, remedial actions #2 and #5 may beperformed generally together at action points 522, and their effectsmonitored during feedback window 523. Remedial action #4 may then beperformed at action point 524 and its effect monitored during feedbackwindow 525. If, as depicted in FIG. 5B, monitored performance duringfeedback window 524 drops, remedial actions #2 and #5 may be backed outat back out point 540.

It will be appreciated by one of ordinary skill in the art that it maybe problematic to accurately detect and/or quantify the effects ofinterference on the performance of a mobile network cell 10,particularly as at least some, but not necessarily all, of suchinference may be emanate from mobile network cell 10 itself; i.e., thesource of the interference may be internal, external or both. Inaccordance with embodiments described herein, interference and/or itssource may be detected/deduced per observation of one or more MKPI.

Reference is now made to FIGS. 6A-C which are graphs of observedEc/No/RSCP rates for mobile network cells 10 experiencing 100% loadunder different power settings: 30 W, 40 W and 60 W, respectively,without external interference. Lines 610 A-C may therefore illustrate abaseline expected relationship between Ec/No and RSCP for eachrespective power setting. It will be appreciated that in a productionenvironment the baseline for may differ somewhat for each mobile networkcell 10, due to differences in topology, urban/rural environment,weather, model numbers, equipment degradation, etc. However, it will beappreciated that a baseline may derived for a given mobile network cell10 or group of cells in a generally similar manner.

Using lines 610 A-C as a baseline, an observation “above” lines 610 A-C,i.e., in areas 620 A-C, would therefore indicate performance exceedingexpectations, presumably enabled by unusual atmospheric conditions or aparadigm shift in the specific environment. An observation in areas 630A-C would indicate degradation of performance that could not beattributed to internally sourced interference, since that would beaccounted for within the framework of the established baseline. AnEc/No/RSCP in area 620 A-C may therefore be used by classifier 130 todetect downlink interference. It will be appreciated by one of ordinaryskill in the art that baseline expectations may also be determined forloads of less than 100%, either by observation and/or extrapolationbased on pre-existing observations.

Reference is now made to FIG. 7 which is a graph of observed RSCP,received total wideband power (RTWP), and total drops over time in anexemplary mobile network cell 10. It will be appreciated by one ofordinary skill in the art that over time while there may be little or nocorrelation between RSCP and drops, there may be an observablecorrelation between RTWP and drops such that it may be reasonablyinferred that a significant increase/decrease in RTWP may generally tendto result in a corresponding decrease/increase in drops. In accordancewith embodiments described herein, an observed change in RTWP in tandemwith relatively static RSCP may be a function of uplink interference. Asinterference increases, RTWP may decrease and drops may increase; whileinterference decreases, RTWP may increase and drops may decrease.Accordingly, classifier 130 may use RTWP observations as a proxyindicator for the detection of uplink interference.

Using the methods described herein, classifier 130 may be configured toautonomously perform root cause analysis to identify whether degradedperformance indicated by a decrease in RTWP or RSCP is due to uplink ordownlink causes. Furthermore, classifier 130 may determine whether thedegraded performance correlates more with internal interference, loadissues of the mobile network cell 10 with degraded performance, loadissues associated with other mobile network cells 10, or even externalnetwork interferences. It will be appreciated by one of ordinary skillin the art that CCO manager 150 may then determine remedial actions tobe performed accordingly, for example, at least according to mapping ofpossible actions to use case presented in FIG. 4.

It is appreciated that software components of the embodiments of thedisclosure may, if desired, be implemented in ROM (read only memory)form. The software components may, generally, be implemented inhardware, if desired, using conventional techniques. It is furtherappreciated that the software components may be instantiated, forexample: as a computer program product or on a tangible medium. In somecases, it may be possible to instantiate the software components as asignal interpretable by an appropriate computer, although such aninstantiation may be excluded in certain embodiments of the disclosure.

It is appreciated that various features of the embodiments of thedisclosure which are, for clarity, described in the contexts of separateembodiments may also be provided in combination in a single embodiment.Conversely, various features of the embodiments of the disclosure whichare, for brevity, described in the context of a single embodiment mayalso be provided separately or in any suitable subcombination.

It will be appreciated by persons skilled in the art that theembodiments of the disclosure are not limited by what has beenparticularly shown and described hereinabove. Rather the scope of theembodiments of the disclosure is defined by the appended claims andequivalents thereof:

What is claimed is:
 1. A method implemented on a computing device, themethod comprising: classifying a current coverage and capacity (CCO)status according to a multiplicity of performance factors for each of amultiplicity of mobile network cells, wherein said multiplicity ofperformance factors includes at least one of uplink interference ordownlink interference; clustering said mobile network cells into cellclusters based on at least said classifying and proximity of said mobilenetwork cells to each other; based at least on said performance factors,identifying at least one problem cluster from among said cell clusters;identifying at least one underperforming master key performanceindicator (MKPI) for said at least one problem cluster; and instructingsaid mobile network cells in said at least one problem cluster toperform at least one remedial action to address at least one of saidperformance factors to improve performance according to said at leastone underperforming MKPI.
 2. The method according to claim 1 and alsocomprising: monitoring said at least one underperforming MKPI in saidmobile network cells in said at least one problem cluster afterperformance of said at least one remedial action; and upon determiningaccording to said monitoring that said at least one underperforming MKPIfalls below a definable back out threshold, instructing said mobilenetwork cells in said problem cluster to back out said at least oneremedial action.
 3. The method according to claim 2 and also comprising:upon determining according to said monitoring that said at least oneunderperforming MKPI falls below a definable corrective actionthreshold, instructing at least one of said mobile network cells in saidat least one problem cluster to perform at least one corrective action.4. The method according to claim 2 and wherein: said at least oneremedial action is at least two remedial actions; said instructingcomprises instructing said mobile network cells to perform each of saidat least two remedial actions separately according to a schedule; andsaid monitoring comprises: defining a feedback window for each of saidat least two remedial actions according to said schedule, monitoringsaid at least one underperforming MKPI in said mobile network cells insaid at least one problem cluster in each said feedback window afterperformance of each of said at least two remedial actions, and for eachsaid feedback window, upon determining that said at least oneunderperforming MKPI falls below a definable back out threshold,instructing said mobile network cells in said problem cluster to backout a most recently performed remedial action from said at least tworemedial actions.
 5. The method according to claim 2 and wherein: saidat least one remedial action is at least three remedial actions;grouping said at least three remedial actions into at least two groupsof remedial actions; said instructing comprises instructing said mobilenetwork cells to perform each of said at least two groups of remedialactions separately according to a schedule; and said monitoringcomprises: defining a feedback window for each of said at least twogroups of remedial actions according to said schedule, monitoring saidat least one underperforming MKPI in said mobile network cells in saidat least one problem cluster in each said feedback window afterperformance of each of said at least two groups of remedial actions, andfor each said feedback window, upon determining that said at least oneunderperforming MKPI falls below a definable back out threshold,instructing said mobile network cells in said problem cluster to backout a most recently performed group of remedial actions from said atleast two groups of remedial actions.
 6. The method according to claim 1and wherein: said multiplicity of performance factors also includes atleast one of downlink coverage or downlink load.
 7. The method accordingto claim 6 and wherein said downlink coverage is assessed in terms ofwhether observed received signal code power (RSCP) is less than or equalto a definable threshold.
 8. The method according to claim 1 and whereinsaid uplink interference is assessed in terms of whether an observedreceived signal strength indication (RSSI) is greater than or equal to adefinable threshold.
 9. The method according to claim 6 and wherein saiddownlink load is assessed in terms of whether downlink load utilizationexceeds a definable threshold.
 10. The method according to claim 1 andwherein said downlink interference is assessed in terms of whetherreceived signal code power (RSCP) exceeds a first definable threshold,while energy per chip/noise spectral density (Ec/No) is less than adefinable threshold.
 11. The method according to claim 6 and whereinsaid clustering comprises: defining a core cluster, wherein each of saidmobile network cells in said core cluster is in neighboring proximitywith at least another of said mobile network cells with at least one ofsaid performance factors in common.
 12. The method according to claim 11and wherein at least another performance factor of said performancefactors is not in common.
 13. The method according to claim 11 andwherein said clustering further comprises: defining a cluster envelopewherein said mobile network cells in said cluster envelope are inneighboring proximity to said core cluster; and forming said cellcluster from said core cluster and said cluster envelope.
 14. The methodaccording to claim 6 and wherein said at least one remedial action toaddress said downlink coverage performance factor is to adjust at leastone of: antenna tunings, downlink dedicated channel power, codecmanagement, or active/idle mobility settings.
 15. The method accordingto claim 1 and wherein said at least one remedial action to address saiduplink interference performance factor is to adjust at least one of:antenna tunings, uplink noise rise, or uplink power control.
 16. Themethod according to claim 6 and wherein said at least one remedialaction to address said downlink load performance factor is to adjust atleast one of: antenna tunings, downlink control channels power, downlinkdedicated channel power, channel switching, codec management, softhandover factors, active/idle mobility settings, tracking areas, orpaging settings.
 17. The method according to claim 1 and wherein said atleast one remedial action to address said downlink interferenceperformance factor is to adjust at least one of: antenna tunings,downlink control channels power, downlink dedicated channel power,channel switching, codec management, or soft handover factors.
 18. Themethod according to claim 3 and wherein said at least one correctiveaction is to increase the power curve (power per radio access bearerRAB) for said at least one of said mobile network cells in said at leastone problem cluster.
 19. A system implemented on a computing devicecomprising: means for classifying a current coverage and capacity (CCO)status according to a multiplicity of performance factors for amultiplicity of mobile network cells, wherein said multiplicity ofperformance factors includes at least downlink coverage, uplinkinterference, downlink load, and downlink interference; means forclustering said mobile network cells into cell clusters based on atleast said classifying and proximity of said mobile network cells toeach other; means for identifying at least one problem cluster fromamong said cell clusters at least according to said classifying; meansfor identifying at least one underperforming master key performanceindicator (MKPI) for said at least one problem cluster; and means forinstructing said mobile network cells in said at least one problemcluster to perform at least one remedial action to address at least oneof said performance factors to improve performance according to said atleast one underperforming MKPI.
 20. A coverage and capacity (CCO) servercomprising: a processor; an I/O module operative to at least: sendinstructions to a multiplicity of mobile network cells, and receivefeedback regarding performance factors of said multiplicity of mobilenetwork cells, wherein said multiplicity of performance factors includesat least downlink coverage, uplink interference, downlink load, anddownlink interference; a classifier to be executed by said processor andoperative to classify said multiplicity of mobile network cellsaccording to said performance factors; a clusterer to be executed bysaid processor and operative to cluster said multiplicity of mobilenetwork cells into cell clusters based on said classifying and proximityof said multiplicity of mobile network cells to each other; and a CCOmanager application to be executed by said processor and operative to:identify a problem cluster from among said cell clusters at leastaccording to said classifying, identify at least one underperformingmaster key performance indicator (MKPI) for said problem cluster, andinstruct said multiplicity of mobile network cells in said problemcluster to perform at least one remedial action to improve said at leastone underperforming MKPI.
 21. A method implemented on a computingdevice, the method comprising: classifying a current coverage andcapacity (CCO) status according to a multiplicity of performance factorsfor a multiplicity of mobile network cells; clustering said mobilenetwork cells into cell clusters based on at least said classifying andproximity of said mobile network cells to each other; based at least onsaid performance factors, identifying at least one problem cluster fromamong said cell clusters; identifying at least one underperformingmaster key performance indicator (MKPI) for said at least one problemcluster; and instructing said mobile network cells in said at least oneproblem cluster to perform at least two remedial actions to address atleast one of said performance factors to improve performance accordingto said at least one underperforming MKPI, wherein each of said at leasttwo remedial actions is to be performed separately according to aschedule; defining a feedback window for each of said at least tworemedial actions according to said schedule; monitoring said at leastone underperforming MKPI in said mobile network cells in said at leastone problem cluster in each said feedback window after performance ofeach of said at least two remedial actions; and for each said feedbackwindow, upon determining according to said monitoring that said at leastone underperforming MKPI falls below a definable back out threshold,instructing said mobile network cells in said problem cluster to backout a most recently performed remedial action from said at least tworemedial actions.
 22. A system implemented on a computing devicecomprising: means for classifying a current coverage and capacity (CCO)status according to a multiplicity of performance factors for amultiplicity of mobile network cells; means for clustering said mobilenetwork cells into cell clusters based on at least said classifying andproximity of said mobile network cells to each other; means foridentifying based at least on said performance factors at least oneproblem cluster from among said cell clusters; means for identifying atleast one underperforming master key performance indicator (MKPI) forsaid at least one problem cluster; and means for instructing said mobilenetwork cells in said at least one problem cluster to perform at leasttwo remedial actions to address at least one of said performance factorsto improve performance according to said at least one underperformingMKPI, wherein each of said at least two remedial actions is to beperformed separately according to a schedule; means for defining afeedback window for each of said at least two remedial actions accordingto said schedule; means monitoring said at least one underperformingMKPI in said mobile network cells in said at least one problem clusterin each said feedback window after performance of each of said at leasttwo remedial actions; and means for instructing said mobile networkcells in said problem cluster to back out a most recently performedremedial action from said at least two remedial actions upon determiningaccording to said monitoring that said MKPI falls below a definable backout threshold.